HealthcareLCA: an open-access living database of health-care environmental impact assessments
Bibliographic record
Abstract
Anthropogenic environmental change negatively effects human health and is increasing health-care system demand. Paradoxically, the provision of health care, which itself is a substantial contributor to environmental degradation, is compounding this problem. There is increasing willingness to transition towards sustainable health-care systems globally and ensuring that strategy and action are informed by best available evidence is imperative. In this Personal View, we present an interactive, open-access database designed to support this effort. Functioning as a living repository of environmental impact assessments within health care, the HealthcareLCA database collates 152 studies, predominantly peer-reviewed journal articles, into one centralised and publicly accessible location, providing impact estimates (currently totalling 3671 numerical values) across 1288 health-care products and processes. The database brings together research generated over the past two decades and indicates exponential field growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.023 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.088 | 0.025 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".